Abstract
With a huge increase in computational power, Traffic Predictive Analysis has seen various improvements in the recent years. Additionally, this field is experimenting an increase in available data, which allows to produce more precise forecasting and classification models. However, this means that the available data has also seen a huge increase in terms of storage size. Data Stream Mining provides a brand new approach to data processing, allowing to create adaptive, incremental models that do not need huge amounts of storage size, as the data is processed as it is received. In this communication, we will explore the state of the art and the first research efforts that can be found in this direction.
Work cofinanced by the Agencia Canaria de Investigación, Innovación y Sociedad de la Información from Consejería de Economía, Industria, Comercio y Conocimiento and by the European Social Fund (ESF), Programa Operativo Integrado de Canarias 2014–2020, Eje 3 Tema Prioritario 74 (85%).
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Guerra-Montenegro, J., Sánchez-Medina, J. (2020). Traffic Predictive Analysis Through Data Stream Mining. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2019. EUROCAST 2019. Lecture Notes in Computer Science(), vol 12014. Springer, Cham. https://doi.org/10.1007/978-3-030-45096-0_24
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DOI: https://doi.org/10.1007/978-3-030-45096-0_24
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